Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Analyzing large scale data has emerged as an important activity for many organizations in the past few years. This large scale data analysis is facilitated by the MapReduce programming and execution model and its implementations, most notably Hadoop. Users of MapReduce often have analysis tasks that are too complex to express as individual MapReduce jobs. Instead, they use high-level query languages such as Pig, Hive, or Jaql to express their complex tasks. The compilers of these languages translate queries into workflows of MapReduce jobs. Each job in these workflows reads its input from the distributed file system used by the MapReduce system and produces output that is stored in this distributed file system and read as input by the next job in the workflow. The current practice is to delete these intermediate results from the distributed file system at the end of executing the workflow. One way to improve the performance of workflows of MapReduce jobs is to keep these intermediate results and reuse them for future workflows submitted to the system. In this paper, we present ReStore , a system that manages the storage and reuse of such intermediate results. ReStore can reuse the output of whole MapReduce jobs that are part of a workflow, and it can also create additional reuse opportunities by materializing and storing the output of query execution operators that are executed within a MapReduce job. We have implemented ReStore as an extension to the Pig dataflow system on top of Hadoop, and we experimentally demonstrate significant speedups on queries from the PigMix benchmark.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it